Statistical inference for rough volatility: Central limit theorems

In recent years, there has been substantive empirical evidence that stochastic volatility is rough. In other words, the local behavior of stochastic volatility is much more irregular than semimartingales and resembles that of a fractional Brownian motion with Hurst parameter $H<0.5$. In this paper, we derive a consistent and asymptotically mixed normal estimator of $H$ based on high-frequency price observations. In contrast to previous works, we work in a semiparametric setting and do not assume any a priori relationship between volatility estimators and true volatility. Furthermore, our estimator attains a rate of convergence that is known to be optimal in a minimax sense in parametric rough volatility models

A mechanical autonomous stochastic heat engine

Stochastic heat engines are devices that generate work from random thermal motion using a small number of highly fluctuating degrees of freedom. Proposals for such devices have existed for more than a century and include the Maxwell demon and the Feynman ratchet. Only recently have they been demonstrated experimentally, using e.g., thermal cycles implemented in optical traps. However, the recent demonstrations of stochastic heat engines are nonautonomous, since they require an external control system that prescribes a heating and cooling cycle, and consume more energy than they produce. This Report presents a heat engine consisting of three coupled mechanical resonators (two ribbons and a cantilever) subject to a stochastic drive. The engine uses geometric nonlinearities in the resonating ribbons to autonomously convert a random excitation into a low-entropy, nonpassive oscillation of the cantilever. The engine presents the anomalous heat transport property of negative thermal conductivity, consisting in the ability to passively transfer energy from a cold reservoir to a hot reservoir

Microchamber Cultures of Bladder Cancer: A Platform for Characterizing Drug Responsiveness and Resistance in PDX and Primary Cancer Cells.

Precision cancer medicine seeks to target the underlying genetic alterations of cancer; however, it has been challenging to use genetic profiles of individual patients in identifying the most appropriate anti-cancer drugs. This spurred the development of patient avatars; for example, patient-derived xenografts (PDXs) established in mice and used for drug exposure studies. However, PDXs are associated with high cost, long development time and low efficiency of engraftment. Herein we explored the use of microfluidic devices or microchambers as simple and low-cost means of maintaining bladder cancer cells over extended periods of times in order to study patterns of drug responsiveness and resistance. When placed into 75 µm tall microfluidic chambers, cancer cells grew as ellipsoids reaching millimeter-scale dimeters over the course of 30 days in culture. We cultured three PDX and three clinical patient specimens with 100% success rate. The turn-around time for a typical efficacy study using microchambers was less than 10 days. Importantly, PDX-derived ellipsoids in microchambers retained patterns of drug responsiveness and resistance observed in PDX mice and also exhibited in vivo-like heterogeneity of tumor responses. Overall, this study establishes microfluidic cultures of difficult-to-maintain primary cancer cells as a useful tool for precision cancer medicine

Brief Announcement: A formalism for consistency and partial replication

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Computational modelling of solvent effects in a prolific solvatomorphic porous organic cage

Crystal structure prediction methods can enable the in silico design of functional molecular crystals, but solvent effects can have a major influence on relative lattice energies sometimes thwarting predictions. This is particularly true for porous solids, where solvent included in the pores can have an important energetic contribution. Here we present a Monte Carlo solvent insertion procedure for predicting the solvent filling of porous structures from crystal structure prediction landscapes, tested using a highly solvatomorphic porous organic cage molecule, CC1. We use this method to rationalise the fact that the predicted global energy minimum structure for CC1 is never observed from solvent crystallisation. We also explain the formation of three different solvatomorphs of CC1 from three structurally-similar chlorinated solvents. Calculated solvent stabilisation energies are found to correlate with experimental results from thermogravimetric analysis, suggesting a future computational framework for a priori materials design that includes solvation effects

Porous organic cages for sulfur hexafluoride separation

A series of porous organic cages is examined for the selective adsorption of sulfur hexafluoride (SF6) over nitrogen. Despite lacking any metal sites, a porous cage, CC3, shows the highest SF6/N2 selectivity reported for any material at ambient temperature and pressure, which translates to real separations in a gas breakthrough column. The SF6 uptake of these materials is considerably higher than would be expected from the static pore structures. The location of SF6 within these materials is elucidated by X-ray crystallography, and it is shown that cooperative diffusion and structural rearrangements in these molecular crystals can rationalize their superior SF6/N2 selectivity

Sulfone-containing covalent organic frameworks for photocatalytic hydrogen evolution from water

Nature uses organic molecules for light harvesting and photosynthesis, but most man-made water splitting catalysts are inorganic semiconductors. Organic photocatalysts, while attractive because of their synthetic tunability, tend to have low quantum efficiencies for water splitting. Here we present a crystalline covalent organic framework (COF) based on a benzo-bis(benzothiophene sulfone) moiety that shows a much higher activity for photochemical hydrogen evolution than its amorphous or semicrystalline counterparts. The COF is stable under long-term visible irradiation and shows steady photochemical hydrogen evolution with a sacrificial electron donor for at least 50 hours. We attribute the high quantum efficiency of fused-sulfone-COF to its crystallinity, its strong visible light absorption, and its wettable, hydrophilic 3.2 nm mesopores. These pores allow the framework to be dye-sensitized, leading to a further 61% enhancement in the hydrogen evolution rate up to 16.3 mmol g −1 h −1 . The COF also retained its photocatalytic activity when cast as a thin film onto a support

Modular and predictable assembly of porous organic molecular crystals

Nanoporous molecular frameworks are important in applications such as separation, storage and catalysis. Empirical rules exist for their assembly but it is still challenging to place and segregate functionality in three-dimensional porous solids in a predictable way. Indeed, recent studies of mixed crystalline frameworks suggest a preference for the statistical distribution of functionalities throughout the pores rather than, for example, the functional group localization found in the reactive sites of enzymes. This is a potential limitation for 'one-pot' chemical syntheses of porous frameworks from simple starting materials. An alternative strategy is to prepare porous solids from synthetically preorganized molecular pores. In principle, functional organic pore modules could be covalently prefabricated and then assembled to produce materials with specific properties. However, this vision of mix-and-match assembly is far from being realized, not least because of the challenge in reliably predicting three-dimensional structures for molecular crystals, which lack the strong directional bonding found in networks. Here we show that highly porous crystalline solids can be produced by mixing different organic cage modules that self-assemble by means of chiral recognition. The structures of the resulting materials can be predicted computationally, allowing in silico materials design strategies. The constituent pore modules are synthesized in high yields on gram scales in a one-step reaction. Assembly of the porous co-crystals is as simple as combining the modules in solution and removing the solvent. In some cases, the chiral recognition between modules can be exploited to produce porous organic nanoparticles. We show that the method is valid for four different cage modules and can in principle be generalized in a computationally predictable manner based on a lock-and-key assembly between modules

Benefits and limitations of implementing Chronic Care Model (CCM) in primary care programs: a systematic review

Background: Chronic Care Model (CCM) has been developed to improve patients' health care by restructuring health systems in a multidimensional manner. This systematic review aims to summarize and analyse programs specifically designed and conducted for the fulfilment of multiple CCM components. We have focused on programs targeting diabetes mellitus, hypertension and cardiovascular disease. Method and results: This review was based on a comprehensive literature search of articles in the PubMed database that reported clinical outcomes. We included a total of 25 eligible articles. Evidence of improvement in medical outcomes and the compliance of patients with medical treatment were reported in 18 and 14 studies, respectively. Two studies demonstrated a reduction of the medical burden in terms of health service utilization, and another two studies reported the effectiveness of the programs in reducing the risk of heart failure and other cardiovascular diseases. However, CCMs were still restricted by limited academic robustness and social constraints when they were implemented in primary care. Higher professional recognition, tighter system collaborations and increased financial support may be necessary to overcome the limitations of, and barriers to CCM implementation. Conclusion: This review has identified the benefits of implementing CCM, and recommended suggestions for the future development of CCM